Housing Justice - Rent Exploitation
Sokona Mangane
2022-07-06
We downloaded data from Census.gov using the get_acs function, did some data wrangling and cleaning, wrote them as a csv file so that we can import them into RMarkdown
#code_folding: "hide" (hides the code but ppl can see the code if they want to)
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE) #doesn't show the messages or the warings, only code output, must be its in own code chunk to apply to all the subsequent ones#rent exploitation is another way racial capitalism works to extract resources from the black community, hidden from view, how are these things still operating but are less visible to the average viewer?
#importing data
library(readr)
library(tidycensus)
library(tidyverse)
library(tigris)
library(leaflet)
library(sf)
library(DT)
library(GGally)
library(RColorBrewer)
ACS_Housing_Data <- read_csv("Housing_Data.csv")
Income_By_Tenure <- read_csv("Income_By_Tenure.csv")
Tenure <- read_csv("Tenure.csv")
Tenure_By_Income <- read_csv("Tenure_By_Income.csv")
Tenure_By_Race <- read_csv("Tenurebyrace.csv")
Poverty_By_Race <- read_csv("PovertyByRace.csv")
cvl_rva_measures <- read_csv("cvl_rva_measures.csv")
race <- c("White","Black", "Asian", "Hispanic or Latino")Basic summary statistics:
How many renters are there in each county? of which demographics?
Petersburg, Hopewell, Charlottesville and Richmond have the highest percentage of renters.
# Tenure <- Tenure[, 3:9]
# Tenure <- full_join(Tenure, Tenure_By_Race, by = "County_fips")
Tenure_By_Race_Perc <- Tenure_By_Race %>%
mutate(Perc_rentersE = (RentersE/Total_OccupantsE), Perc_ownersE = (OwnersE/Total_OccupantsE)) %>%
group_by(County) %>%
summarize(Renters = median(Perc_rentersE, na.rm = TRUE), Owners = median(Perc_ownersE, na.rm = TRUE), White_Renters = median((White_rentersE/RentersE), na.rm = TRUE), White_Owners = median((White_ownersE/OwnersE), na.rm = TRUE), Black_Renters = median((Black_rentersE/RentersE), na.rm = TRUE), Black_Owners = median((Black_ownersE/OwnersE), na.rm = TRUE), NativeAm_Owners = median((NativeAm_ownersE/OwnersE), na.rm = TRUE), NativeAm_Renters = median((NativeAm_rentersE/RentersE), na.rm = TRUE), Asian_Owners = median((Asian_ownersE/OwnersE), na.rm = TRUE), Asian_Renters = median((Asian_rentersE/RentersE), na.rm = TRUE), PacificIslander_Owners = median((PacificIslander_ownerE/OwnersE), na.rm = TRUE), PacificIslander_Renters = median((PacificIslander_renterE/RentersE), na.rm = TRUE), HispanicLatino_Owners = median((HispanicLatino_ownerE/OwnersE), na.rm = TRUE), HispanicLatino_Renters = median((HispanicLation_renterE/RentersE), na.rm = TRUE)) %>%
mutate(across(c(2:14), scales::percent)) %>%
arrange(desc(Renters)) %>%
select(1:3,4,6,9,11,13,15,5,7,8,10,12,14)
sketch = htmltools::withTags(table(
class = 'display',
thead(
tr(
th(rowspan = 2, 'County'),
th(colspan = 6, 'Renters'),
th(colspan = 6, 'Owners')
),
tr(
lapply(c('White_Renters', 'Black_Renters', 'NativeAm_Renters', 'Asian_Renters', "PacificIslander_Renters", "HispanicLatino_Renters", 'White_Owners', 'Black_Owners', 'NativeAm_Owners', 'Asian_Owners', "PacificIslander_Owners", "HispanicLatino_Owners"), th)
)
)
))
DT_Tenure_By_Race <- Tenure_By_Race_Perc[,c(1,4:15)]
datatable(DT_Tenure_By_Race,
caption = htmltools::tags$caption(
style = 'caption-side: bottom; text-align: center;',
'Table 1: ', htmltools::em('Median Percentage of Renters and Owners in Each County (by Demographics)')),
container = sketch,
rownames = FALSE,
extensions = 'Buttons',
options = list(dom='Bfrtip',
buttons=c('copy', 'csv', 'excel', 'print', 'pdf')
)
)#how to make data mpt extend screen and show on next page
#can do a scatterplot matrix and DataTables to provide summary statistics
#maps, leaflets
#modelsTenure_perc_by_race <- Tenure_By_Race %>%
mutate(Perc_WRenters = (White_rentersE/RentersE), Perc_WOwners = (White_ownersE/OwnersE), Perc_BRenters = (Black_rentersE/RentersE), Perc_BOwners = (Black_ownersE/OwnersE), Perc_NativeAmOwners = (NativeAm_ownersE/OwnersE), Perc_NativeAmRenters = (NativeAm_rentersE/RentersE), Perc_AsianOwners = (Asian_ownersE/OwnersE), Perc_AsianRenters = (Asian_rentersE/RentersE), Perc_PacificIslanderOwner = (PacificIslander_ownerE/OwnersE), Perc_PacificIslanderRenter = (PacificIslander_renterE/RentersE), Perc_HispanicLatinoOwner = (HispanicLatino_ownerE/OwnersE), Perc_HispanicLationRenterE = (HispanicLation_renterE/RentersE)) %>%
select(1:5, 48:59)
Tenure_perc_by_race <- Tenure_perc_by_race %>%
pivot_longer(., cols = c(Perc_AsianOwners, Perc_WRenters, Perc_WOwners, Perc_BOwners, Perc_BRenters, Perc_NativeAmOwners, Perc_NativeAmRenters, Perc_AsianRenters, Perc_PacificIslanderOwner, Perc_PacificIslanderRenter, Perc_HispanicLatinoOwner, Perc_HispanicLationRenterE), names_to = "Variable", values_to = "Percentage (Estimated)") %>%
mutate(Race = case_when(
str_detect(Variable, "Asian") ~ "Asian",
str_detect(Variable, "B") ~ "Black",
str_detect(Variable, "NativeAm") ~ "Native American",
str_detect(Variable, "PacificIslander") ~ "Pacific Islander",
str_detect(Variable, "Hispanic") ~ "Hispanic or Latino",
str_detect(Variable, "WRenters") ~ "White",
str_detect(Variable, "WOwners") ~ "White"
),
Variable = case_when(
str_detect(Variable, "Owner") ~ "Owner",
str_detect(Variable, "Renter") ~ "Renter"
))
Tenure_perc_by_race <- rename(Tenure_perc_by_race, Tenure_Type = Variable)
# Tenure_perc_by_race$`Percentage (Estimated)` <- round(Tenure_perc_by_race$`Percentage (Estimated)`, 2)
# Tenure_perc_by_race %>%
# filter(Tenure_Type == "Renter" & Race %in% race) %>%
# ggplot(aes(x = Tenure_Type, y = `Percentage (Estimated)`)) +
# geom_boxplot() +
# facet_wrap(~Race)
#Same graph different output
Tenure_perc_by_race %>%
filter(Tenure_Type == "Renter" & Race %in% race ) %>%
ggplot(aes(x = Tenure_Type, y = `Percentage (Estimated)`, fill = Race)) +
scale_fill_viridis_d() +
geom_boxplot() +
facet_wrap(~Region) +
labs(title = "Racial Composition of Renters in Charlottesville and Richmond, Virginia", x = "Tenure Type")
#Chartlottesville has way less data points
ACS_Housing_Data %>%
group_by(Region) %>%
summarise(sum = n(), Percent = n()/317)## # A tibble: 2 × 3
## Region sum Percent
## <chr> <int> <dbl>
## 1 Charlottesville 64 0.202
## 2 Richmond 253 0.798Tenure_perc_by_race %>%
filter(Tenure_Type == "Renter") %>%
group_by(County, Race) %>%
select(4,7,8) %>%
summarize(Perc_Renters = median(Tenure_perc_by_race$`Percentage (Estimated)`, na.rm = TRUE)) %>%
arrange(desc(Perc_Renters))## # A tibble: 66 × 3
## # Groups: County [11]
## County Race Perc_Renters
## <chr> <chr> <dbl>
## 1 Albemarle Asian 0.0119
## 2 Albemarle Black 0.0119
## 3 Albemarle Hispanic or Latino 0.0119
## 4 Albemarle Native American 0.0119
## 5 Albemarle Pacific Islander 0.0119
## 6 Albemarle White 0.0119
## 7 Charlottesville Asian 0.0119
## 8 Charlottesville Black 0.0119
## 9 Charlottesville Hispanic or Latino 0.0119
## 10 Charlottesville Native American 0.0119
## # … with 56 more rows(backgournd)
relationship between demographics of renters and rent expoitation?
Tenure_and_Housing_Data <- full_join(Tenure_perc_by_race, ACS_Housing_Data, by = "GEOID") %>%
select(1,6:30)
Tenure_and_Housing_Data %>%
filter(Tenure_Type == "Renter" & Race %in% race) %>%
ggplot(aes(x = `Percentage (Estimated)`, y = RentTaxRatio, color = Region.y)) +
geom_point(alpha = 0.3) +
geom_smooth(method = "lm") +
facet_wrap(~Race) +
labs(title = "Demographic of Renter vs Rent Exploitation in Charlottesville and Richmond, VA", x = "Percentage of Renter (Estimated)", y = "Rent to Tax Ratio", color = "Region") 
# Tenure_and_Housing_Data %>%
# filter(Tenure_Type == "Renter" & Race %in% race) %>%
# ggplot(aes(x = `Percentage (Estimated)`, y = RentTaxRatio)) +
# geom_point(alpha = 0.3) +
# geom_smooth(method = "lm") +
# facet_wrap(~Region.y)
#Before, it was facet_wrapped by race and colored based on the tenure typem but because I filtered out for renters, idk which graph looks better and is "easier" to read and understand, should I remove geom_point, even though she told me to add it?#modeling whats above
lm1 <- lm(RentTaxRatio ~ `Percentage (Estimated)`:factor(Race), data = Tenure_and_Housing_Data)
lm1 ##
## Call:
## lm(formula = RentTaxRatio ~ `Percentage (Estimated)`:factor(Race),
## data = Tenure_and_Housing_Data)
##
## Coefficients:
## (Intercept)
## 0.6365
## `Percentage (Estimated)`:factor(Race)Asian
## -0.2752
## `Percentage (Estimated)`:factor(Race)Black
## 0.1288
## `Percentage (Estimated)`:factor(Race)Hispanic or Latino
## 0.5291
## `Percentage (Estimated)`:factor(Race)Native American
## -0.2271
## `Percentage (Estimated)`:factor(Race)Pacific Islander
## 2.1519
## `Percentage (Estimated)`:factor(Race)White
## -0.0360What is the average rent, income, and real estate taxes paid in each County?
Highest rent: Fluvanna, Chesterfield, Henrico and Albemarle Highest median real estate taxes: Albemare, Charlottesville, Richmond, and Chesterfeild
ACS_Housing_Data |>
group_by(County) |>
summarize(Median_rent = median(MedianRentE, na.rm = TRUE), Median_tax = median(MedianTaxesE, na.rm = TRUE), Median_income = median(MedianIncomeE, na.rm = TRUE))## # A tibble: 11 × 4
## County Median_rent Median_tax Median_income
## <chr> <dbl> <dbl> <dbl>
## 1 Albemarle 1323 2657 55964
## 2 Charlottesville 1181 2622. 40104
## 3 Chesterfield 1333 1935 60040
## 4 Fluvanna 1419 1786 49581
## 5 Greene 974. 1636. 51240.
## 6 Henrico 1214 1923 53133
## 7 Hopewell 910. 1104. 28625
## 8 Louisa 875 1432 46964
## 9 Nelson 918. 1357 44754
## 10 Petersburg City 952 1067 34167
## 11 Richmond City 1085 2148 37975Census Tract 4.01 - Friendship Court
Census Tract 6 - Bice House
What is the average rent to tax ratio in each county?
Highest Rent Tax Ratio: Petersburg City, Chesterfeild, and Nelson
ACS_Housing_Data$RentTaxRatio <- round(ACS_Housing_Data$RentTaxRatio, 3)
all<- b <- Tenure_and_Housing_Data %>%
filter(`Percentage (Estimated)` >= 0.5, Race == "Black", Tenure_Type == "Renter") %>%
group_by(Region.y) %>%
summarize(Rent_Tax_RaTio_Black = median(RentTaxRatio, na.rm = TRUE))
b <- Tenure_and_Housing_Data %>%
filter(`Percentage (Estimated)` >= 0.5, Race == "Black", Tenure_Type == "Renter") %>%
group_by(Region.y) %>%
summarize(Rent_Tax_RaTio_Black = median(RentTaxRatio, na.rm = TRUE))
all## # A tibble: 2 × 2
## Region.y Rent_Tax_RaTio_Black
## <chr> <dbl>
## 1 Charlottesville 0.962
## 2 Richmond 0.738b## # A tibble: 2 × 2
## Region.y Rent_Tax_RaTio_Black
## <chr> <dbl>
## 1 Charlottesville 0.962
## 2 Richmond 0.738What is the average percent of rent of income in each county? Thus, which county is the most rent burdened?
Richmond, Charlottesville and Nelson are the most rent burdened counties, but no counties appear to be severly rent burdened (more than 50), on average.
ACS_Housing_Data %>%
group_by(County) %>%
summarize(Median_perc_rent_income = median(PercRentBurdenE, na.rm = TRUE)) %>%
arrange(desc(Median_perc_rent_income))## # A tibble: 11 × 2
## County Median_perc_rent_income
## <chr> <dbl>
## 1 Richmond City 32.6
## 2 Charlottesville 31.9
## 3 Hopewell 31.4
## 4 Henrico 28.6
## 5 Albemarle 27.9
## 6 Petersburg City 27.8
## 7 Chesterfield 27.5
## 8 Greene 27
## 9 Louisa 26.6
## 10 Nelson 23.8
## 11 Fluvanna 21.3Here is a more detailed look at who’s rent burded and severely rent burdened in each County, with Richmond still being the most rent burdened.
ACS_Housing_Data <- ACS_Housing_Data %>%
mutate(Rent_Burdened = case_when(
PercRentBurdenE >= 30 & PercRentBurdenE < 50 ~ "Yes",
PercRentBurdenE >= 50 ~"Yes, Severely",
TRUE ~ "No")
)
Rent_Burden_stats <- ACS_Housing_Data %>%
filter(Rent_Burdened != "No") %>%
group_by(Rent_Burdened, County) %>%
summarize(Percent = round((n()/317)*100, 3)) %>%
arrange(desc(Percent))
knitr::kable(Rent_Burden_stats)| Rent_Burdened | County | Percent |
|---|---|---|
| Yes | Richmond City | 13.565 |
| Yes | Henrico | 10.095 |
| Yes | Chesterfield | 6.940 |
| Yes | Albemarle | 3.155 |
| Yes | Charlottesville | 2.208 |
| Yes | Petersburg City | 1.262 |
| Yes, Severely | Richmond City | 1.262 |
| Yes | Hopewell | 0.946 |
| Yes | Louisa | 0.946 |
| Yes, Severely | Chesterfield | 0.946 |
| Yes, Severely | Albemarle | 0.315 |
| Yes, Severely | Fluvanna | 0.315 |
| Yes, Severely | Henrico | 0.315 |
| Yes, Severely | Nelson | 0.315 |
As we can see here, alhtough it may not look like it from the numbers above, a lot of counties (the biggest census tracts too) are rent burdened, some even severely.
counties <- c("Albemarle", "Charlottesville", "Fluvanna", "Greene", "Louisa", "Nelson", "Richmond city", "Henrico", "Chesterfield", "Hopewell", "Petersburg")
countytracts <- tracts(state = "VA", county = counties, year = 2020)##
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|======================================================================| 100%countytracts <- countytracts |>
mutate(GEOID = as.numeric(countytracts$GEOID))
HousingDataSpatial <- full_join(ACS_Housing_Data, countytracts, by = "GEOID") |>
sf::st_as_sf() |>
mutate(INTPTLAT = as.numeric(countytracts$INTPTLAT), INTPTLON = as.numeric(countytracts$INTPTLON))|>
sf::st_transform(crs = '+proj=longlat +datum=WGS84') #one way to reference a CRS, another way (which Claibourne used) is cvl_rents <- st_transform(cvl_rents, 4326) because leaflet expects the crs to be 4326
pal <- colorNumeric("YlOrRd", HousingDataSpatial$PercRentBurdenE, reverse = TRUE)
HousingDataSpatial %>%
leaflet() %>%
addTiles() %>%
addPolygons(color = "black",
fillColor = ~pal(PercRentBurdenE),
fillOpacity = 0.6,
weight = 2,
highlight = highlightOptions(
weight = 3,
fillOpacity = 0.9,
bringToFront = T),
popup = paste0("County: ", HousingDataSpatial$County, "<br>",
"Tract: ", HousingDataSpatial$NAMELSAD, "<br>",
"Percentage of Rent Burden: ", HousingDataSpatial$PercRentBurdenE)) %>%
addLegend(pal = pal,
values = ~PercRentBurdenE,
opacity = 0.7,
title = "Percentage of Rent Burden (2020)",
position = "bottomleft")#FOR POSTER
HousingDataSpatial %>%
filter(Region == "Charlottesville") %>%
ggplot() +
geom_sf(aes(fill=PercRentBurdenE)) +
scale_fill_viridis_c() +
scale_color_viridis_c() +
ggtitle("Degree of Rent Burden in Charlottesville", subtitle = "2020 ACS") +
labs(fill = "Rent as a Percentage of Household Income")
Rent_Burden_rvl_sf <- HousingDataSpatial %>%
filter(Region == "Richmond") %>%
ggplot() +
geom_sf(aes(fill=PercRentBurdenE)) +
scale_fill_viridis_c() +
scale_color_viridis_c() +
ggtitle("Degree of Rent Burden in Richmond", subtitle = "2020 ACS") +
labs(fill = "Rent as a Percentage of Household Income")
ggsave("Degree of Rent Burden in Richmond.png", Rent_Burden_rvl_sf)
Rent_Burdened_Stats_Detailed <- HousingDataSpatial %>%
filter(PercRentBurdenE >= 30) %>%
select(3,12) %>%
arrange(desc(PercRentBurdenE))
knitr::kable(Rent_Burdened_Stats_Detailed)| NAME.x | PercRentBurdenE | geometry |
|---|---|---|
| Census Tract 1002.08, Chesterfield County, Virginia | 51.0 | MULTIPOLYGON (((-77.59176 3… |
| Census Tract 1009.07, Chesterfield County, Virginia | 51.0 | MULTIPOLYGON (((-77.58165 3… |
| Census Tract 1010.03, Chesterfield County, Virginia | 51.0 | MULTIPOLYGON (((-77.87842 3… |
| Census Tract 9501.02, Nelson County, Virginia | 51.0 | MULTIPOLYGON (((-78.90884 3… |
| Census Tract 210, Richmond city, Virginia | 51.0 | MULTIPOLYGON (((-77.40362 3… |
| Census Tract 305.01, Richmond city, Virginia | 51.0 | MULTIPOLYGON (((-77.44895 3… |
| Census Tract 403, Richmond city, Virginia | 51.0 | MULTIPOLYGON (((-77.45652 3… |
| Census Tract 201.03, Fluvanna County, Virginia | 51.0 | MULTIPOLYGON (((-78.34287 3… |
| Census Tract 710.04, Richmond city, Virginia | 51.0 | MULTIPOLYGON (((-77.52794 3… |
| Census Tract 105.01, Albemarle County, Virginia | 51.0 | MULTIPOLYGON (((-78.45505 3… |
| Census Tract 2015.04, Henrico County, Virginia | 51.0 | MULTIPOLYGON (((-77.39592 3… |
| Census Tract 302, Richmond city, Virginia | 48.9 | MULTIPOLYGON (((-77.44727 3… |
| Census Tract 2.02, Charlottesville city, Virginia | 48.6 | MULTIPOLYGON (((-78.50342 3… |
| Census Tract 404, Richmond city, Virginia | 48.3 | MULTIPOLYGON (((-77.46265 3… |
| Census Tract 2001.24, Henrico County, Virginia | 48.3 | MULTIPOLYGON (((-77.63887 3… |
| Census Tract 6, Charlottesville city, Virginia | 47.8 | MULTIPOLYGON (((-78.52363 3… |
| Census Tract 504, Richmond city, Virginia | 47.5 | MULTIPOLYGON (((-77.51961 3… |
| Census Tract 212, Richmond city, Virginia | 47.1 | MULTIPOLYGON (((-77.40902 3… |
| Census Tract 1004.06, Chesterfield County, Virginia | 46.7 | MULTIPOLYGON (((-77.45375 3… |
| Census Tract 203, Richmond city, Virginia | 46.3 | MULTIPOLYGON (((-77.41706 3… |
| Census Tract 402.01, Richmond city, Virginia | 46.0 | MULTIPOLYGON (((-77.45534 3… |
| Census Tract 2016.02, Henrico County, Virginia | 45.9 | MULTIPOLYGON (((-77.42901 3… |
| Census Tract 106.04, Albemarle County, Virginia | 45.3 | MULTIPOLYGON (((-78.47379 3… |
| Census Tract 9505, Louisa County, Virginia | 44.6 | MULTIPOLYGON (((-77.89289 3… |
| Census Tract 708.03, Richmond city, Virginia | 44.0 | MULTIPOLYGON (((-77.5042 37… |
| Census Tract 8104, Petersburg city, Virginia | 43.1 | MULTIPOLYGON (((-77.43129 3… |
| Census Tract 9501.02, Louisa County, Virginia | 43.0 | MULTIPOLYGON (((-77.95314 3… |
| Census Tract 2010.02, Henrico County, Virginia | 42.6 | MULTIPOLYGON (((-77.41507 3… |
| Census Tract 110, Richmond city, Virginia | 41.8 | MULTIPOLYGON (((-77.43683 3… |
| Census Tract 604, Richmond city, Virginia | 41.8 | MULTIPOLYGON (((-77.48407 3… |
| Census Tract 2006, Henrico County, Virginia | 41.8 | MULTIPOLYGON (((-77.48872 3… |
| Census Tract 8204, Hopewell city, Virginia | 41.5 | MULTIPOLYGON (((-77.33777 3… |
| Census Tract 1004.10, Chesterfield County, Virginia | 41.2 | MULTIPOLYGON (((-77.46391 3… |
| Census Tract 104.02, Richmond city, Virginia | 41.2 | MULTIPOLYGON (((-77.46287 3… |
| Census Tract 2011.04, Henrico County, Virginia | 41.2 | MULTIPOLYGON (((-77.39581 3… |
| Census Tract 608, Richmond city, Virginia | 41.1 | MULTIPOLYGON (((-77.44863 3… |
| Census Tract 2001.22, Henrico County, Virginia | 41.1 | MULTIPOLYGON (((-77.63849 3… |
| Census Tract 2007, Henrico County, Virginia | 40.9 | MULTIPOLYGON (((-77.47378 3… |
| Census Tract 1010.14, Chesterfield County, Virginia | 40.5 | MULTIPOLYGON (((-77.67324 3… |
| Census Tract 602, Richmond city, Virginia | 40.5 | MULTIPOLYGON (((-77.44793 3… |
| Census Tract 7, Charlottesville city, Virginia | 39.9 | MULTIPOLYGON (((-78.51737 3… |
| Census Tract 2010.03, Henrico County, Virginia | 39.3 | MULTIPOLYGON (((-77.41168 3… |
| Census Tract 1002.12, Chesterfield County, Virginia | 39.0 | MULTIPOLYGON (((-77.51638 3… |
| Census Tract 5.01, Charlottesville city, Virginia | 38.8 | MULTIPOLYGON (((-78.50552 3… |
| Census Tract 2011.03, Henrico County, Virginia | 38.8 | MULTIPOLYGON (((-77.37542 3… |
| Census Tract 2009.04, Henrico County, Virginia | 38.7 | MULTIPOLYGON (((-77.45269 3… |
| Census Tract 1003, Chesterfield County, Virginia | 38.5 | MULTIPOLYGON (((-77.46161 3… |
| Census Tract 8105, Petersburg city, Virginia | 38.0 | MULTIPOLYGON (((-77.44586 3… |
| Census Tract 108, Richmond city, Virginia | 37.9 | MULTIPOLYGON (((-77.42816 3… |
| Census Tract 1009.35, Chesterfield County, Virginia | 37.5 | MULTIPOLYGON (((-77.6769 37… |
| Census Tract 109.04, Albemarle County, Virginia | 37.5 | MULTIPOLYGON (((-78.52864 3… |
| Census Tract 102.02, Richmond city, Virginia | 37.4 | MULTIPOLYGON (((-77.47942 3… |
| Census Tract 610.01, Richmond city, Virginia | 37.4 | MULTIPOLYGON (((-77.46167 3… |
| Census Tract 1008.06, Chesterfield County, Virginia | 37.1 | MULTIPOLYGON (((-77.47331 3… |
| Census Tract 2008.07, Henrico County, Virginia | 37.0 | MULTIPOLYGON (((-77.44934 3… |
| Census Tract 201, Richmond city, Virginia | 36.9 | MULTIPOLYGON (((-77.4277 37… |
| Census Tract 111.01, Albemarle County, Virginia | 36.9 | MULTIPOLYGON (((-78.72575 3… |
| Census Tract 2.01, Charlottesville city, Virginia | 36.7 | MULTIPOLYGON (((-78.50044 3… |
| Census Tract 605.02, Richmond city, Virginia | 36.5 | MULTIPOLYGON (((-77.49326 3… |
| Census Tract 2004.09, Henrico County, Virginia | 36.5 | MULTIPOLYGON (((-77.53545 3… |
| Census Tract 2004.15, Henrico County, Virginia | 36.4 | MULTIPOLYGON (((-77.54265 3… |
| Census Tract 1005.06, Chesterfield County, Virginia | 36.2 | MULTIPOLYGON (((-77.44617 3… |
| Census Tract 708.02, Richmond city, Virginia | 36.1 | MULTIPOLYGON (((-77.48902 3… |
| Census Tract 2004.13, Henrico County, Virginia | 35.8 | MULTIPOLYGON (((-77.54721 3… |
| Census Tract 109.01, Albemarle County, Virginia | 35.7 | MULTIPOLYGON (((-78.52472 3… |
| Census Tract 111, Richmond city, Virginia | 35.6 | MULTIPOLYGON (((-77.45443 3… |
| Census Tract 2003.05, Henrico County, Virginia | 35.5 | MULTIPOLYGON (((-77.55384 3… |
| Census Tract 1005.07, Chesterfield County, Virginia | 35.4 | MULTIPOLYGON (((-77.46006 3… |
| Census Tract 2004.18, Henrico County, Virginia | 35.4 | MULTIPOLYGON (((-77.5242 37… |
| Census Tract 2014.03, Henrico County, Virginia | 35.3 | MULTIPOLYGON (((-77.31943 3… |
| Census Tract 208, Richmond city, Virginia | 35.0 | MULTIPOLYGON (((-77.41925 3… |
| Census Tract 108.02, Albemarle County, Virginia | 34.9 | MULTIPOLYGON (((-78.53369 3… |
| Census Tract 1008.17, Chesterfield County, Virginia | 34.8 | MULTIPOLYGON (((-77.51583 3… |
| Census Tract 105, Richmond city, Virginia | 34.8 | MULTIPOLYGON (((-77.44418 3… |
| Census Tract 204, Richmond city, Virginia | 34.8 | MULTIPOLYGON (((-77.43291 3… |
| Census Tract 106.03, Albemarle County, Virginia | 34.8 | MULTIPOLYGON (((-78.48515 3… |
| Census Tract 710.02, Richmond city, Virginia | 34.6 | MULTIPOLYGON (((-77.51603 3… |
| Census Tract 2004.12, Henrico County, Virginia | 34.5 | MULTIPOLYGON (((-77.52945 3… |
| Census Tract 1009.12, Chesterfield County, Virginia | 34.4 | MULTIPOLYGON (((-77.6916 37… |
| Census Tract 8101, Petersburg city, Virginia | 34.4 | MULTIPOLYGON (((-77.39573 3… |
| Census Tract 8207, Hopewell city, Virginia | 34.3 | MULTIPOLYGON (((-77.29925 3… |
| Census Tract 2012.05, Henrico County, Virginia | 34.3 | MULTIPOLYGON (((-77.34449 3… |
| Census Tract 8205, Hopewell city, Virginia | 34.2 | MULTIPOLYGON (((-77.31972 3… |
| Census Tract 707, Richmond city, Virginia | 34.2 | MULTIPOLYGON (((-77.52381 3… |
| Census Tract 709.01, Richmond city, Virginia | 34.2 | MULTIPOLYGON (((-77.47859 3… |
| Census Tract 103, Richmond city, Virginia | 34.0 | MULTIPOLYGON (((-77.4492 37… |
| Census Tract 2001.06, Henrico County, Virginia | 33.9 | MULTIPOLYGON (((-77.59446 3… |
| Census Tract 2001.36, Henrico County, Virginia | 33.9 | MULTIPOLYGON (((-77.64026 3… |
| Census Tract 501, Richmond city, Virginia | 33.6 | MULTIPOLYGON (((-77.49483 3… |
| Census Tract 107.01, Albemarle County, Virginia | 33.6 | MULTIPOLYGON (((-78.49932 3… |
| Census Tract 2001.38, Henrico County, Virginia | 33.6 | MULTIPOLYGON (((-77.6074 37… |
| Census Tract 8106, Petersburg city, Virginia | 33.4 | MULTIPOLYGON (((-77.42329 3… |
| Census Tract 106, Richmond city, Virginia | 33.2 | MULTIPOLYGON (((-77.45496 3… |
| Census Tract 710.03, Richmond city, Virginia | 33.1 | MULTIPOLYGON (((-77.52287 3… |
| Census Tract 104.01, Richmond city, Virginia | 33.0 | MULTIPOLYGON (((-77.46416 3… |
| Census Tract 706.02, Richmond city, Virginia | 33.0 | MULTIPOLYGON (((-77.48872 3… |
| Census Tract 505, Richmond city, Virginia | 32.8 | MULTIPOLYGON (((-77.54733 3… |
| Census Tract 103.02, Albemarle County, Virginia | 32.8 | MULTIPOLYGON (((-78.46114 3… |
| Census Tract 109, Richmond city, Virginia | 32.7 | MULTIPOLYGON (((-77.43092 3… |
| Census Tract 1006, Chesterfield County, Virginia | 32.6 | MULTIPOLYGON (((-77.43459 3… |
| Census Tract 4.01, Charlottesville city, Virginia | 32.5 | MULTIPOLYGON (((-78.50536 3… |
| Census Tract 703, Richmond city, Virginia | 32.5 | MULTIPOLYGON (((-77.55451 3… |
| Census Tract 2014.06, Henrico County, Virginia | 32.5 | MULTIPOLYGON (((-77.36603 3… |
| Census Tract 206, Richmond city, Virginia | 32.3 | MULTIPOLYGON (((-77.4214 37… |
| Census Tract 1005.05, Chesterfield County, Virginia | 32.2 | MULTIPOLYGON (((-77.47154 3… |
| Census Tract 1009.26, Chesterfield County, Virginia | 32.1 | MULTIPOLYGON (((-77.66533 3… |
| Census Tract 708.04, Richmond city, Virginia | 32.1 | MULTIPOLYGON (((-77.51436 3… |
| Census Tract 207, Richmond city, Virginia | 31.9 | MULTIPOLYGON (((-77.4182 37… |
| Census Tract 2008.01, Henrico County, Virginia | 31.9 | MULTIPOLYGON (((-77.49403 3… |
| Census Tract 2008.05, Henrico County, Virginia | 31.9 | MULTIPOLYGON (((-77.44088 3… |
| Census Tract 1001.07, Chesterfield County, Virginia | 31.7 | MULTIPOLYGON (((-77.54964 3… |
| Census Tract 1008.14, Chesterfield County, Virginia | 31.7 | MULTIPOLYGON (((-77.51504 3… |
| Census Tract 413, Richmond city, Virginia | 31.7 | MULTIPOLYGON (((-77.48373 3… |
| Census Tract 112.01, Albemarle County, Virginia | 31.6 | MULTIPOLYGON (((-78.83701 3… |
| Census Tract 1009.28, Chesterfield County, Virginia | 31.5 | MULTIPOLYGON (((-77.71673 3… |
| Census Tract 102.01, Richmond city, Virginia | 31.5 | MULTIPOLYGON (((-77.4669 37… |
| Census Tract 9502.01, Louisa County, Virginia | 31.4 | MULTIPOLYGON (((-78.09412 3… |
| Census Tract 2001.31, Henrico County, Virginia | 31.4 | MULTIPOLYGON (((-77.55244 3… |
| Census Tract 2001.52, Henrico County, Virginia | 31.4 | MULTIPOLYGON (((-77.57421 3… |
| Census Tract 4.02, Charlottesville city, Virginia | 31.3 | MULTIPOLYGON (((-78.48534 3… |
| Census Tract 605.01, Richmond city, Virginia | 31.3 | MULTIPOLYGON (((-77.47312 3… |
| Census Tract 209, Richmond city, Virginia | 31.2 | MULTIPOLYGON (((-77.41071 3… |
| Census Tract 1002.11, Chesterfield County, Virginia | 30.8 | MULTIPOLYGON (((-77.52517 3… |
| Census Tract 2001.53, Henrico County, Virginia | 30.8 | MULTIPOLYGON (((-77.57858 3… |
| Census Tract 2005.01, Henrico County, Virginia | 30.8 | MULTIPOLYGON (((-77.51625 3… |
| Census Tract 2017.01, Henrico County, Virginia | 30.6 | MULTIPOLYGON (((-77.32844 3… |
| Census Tract 414, Richmond city, Virginia | 30.5 | MULTIPOLYGON (((-77.47557 3… |
| Census Tract 2004.07, Henrico County, Virginia | 30.5 | MULTIPOLYGON (((-77.5605 37… |
| Census Tract 105.02, Albemarle County, Virginia | 30.4 | MULTIPOLYGON (((-78.46098 3… |
| Census Tract 211, Richmond city, Virginia | 30.3 | MULTIPOLYGON (((-77.41932 3… |
| Census Tract 1005.09, Chesterfield County, Virginia | 30.2 | MULTIPOLYGON (((-77.40572 3… |
| Census Tract 1008.12, Chesterfield County, Virginia | 30.1 | MULTIPOLYGON (((-77.59442 3… |
| Census Tract 1009.29, Chesterfield County, Virginia | 30.1 | MULTIPOLYGON (((-77.65963 3… |
| Census Tract 2005.03, Henrico County, Virginia | 30.1 | MULTIPOLYGON (((-77.5181 37… |
| Census Tract 1004.05, Chesterfield County, Virginia | 30.0 | MULTIPOLYGON (((-77.45335 3… |
relationship between demographics of renters and being rent burndened?
#
# Tenure_and_Housing_Data %>%
# filter(Tenure_Type == "Renter") %>%
# ggplot(aes(x = `Percentage (Estimated)`, y = PercRentBurdenE, color = Tenure_Type)) +
# geom_point(alpha = 0.3) +
# geom_smooth(method = "lm") +
# facet_wrap(~Race)
#different graph, same
Tenure_and_Housing_Data %>%
filter(Tenure_Type == "Renter" & Race %in% race) %>%
ggplot(aes(x = `Percentage (Estimated)`, y = PercRentBurdenE, color = Region.y)) +
scale_color_viridis_d() +
geom_point(alpha = 0.3) +
geom_smooth(method = "lm") +
facet_wrap(~Race) +
labs(title = "Demographic of Renter vs Rent Burden in CVL and RVA", x = "Percentage of Renter (Estimated)", y = "Rent as a Percentage of Income", color = "Region")
#model whats above
lm3 <- lm(PercRentBurdenE ~ Race:`Percentage (Estimated)`, data = Tenure_and_Housing_Data)
lm3##
## Call:
## lm(formula = PercRentBurdenE ~ Race:`Percentage (Estimated)`,
## data = Tenure_and_Housing_Data)
##
## Coefficients:
## (Intercept)
## 30.431
## RaceAsian:`Percentage (Estimated)`
## -3.197
## RaceBlack:`Percentage (Estimated)`
## 3.336
## RaceHispanic or Latino:`Percentage (Estimated)`
## 4.721
## RaceNative American:`Percentage (Estimated)`
## 44.182
## RacePacific Islander:`Percentage (Estimated)`
## -46.827
## RaceWhite:`Percentage (Estimated)`
## -1.090#relationship btw those who are rent burdened and rent tax ratio?
Tenure_and_Housing_Data %>%
ggplot(aes(x = PercRentBurdenE, y = RentTaxRatio)) +
geom_point(alpha = 0.3) +
geom_smooth(method = "lm") +
facet_wrap(~Region.y)
#model whats above
lm5 <- lm(RentTaxRatio ~ PercRentBurdenE, data = Tenure_and_Housing_Data)
lm5##
## Call:
## lm(formula = RentTaxRatio ~ PercRentBurdenE, data = Tenure_and_Housing_Data)
##
## Coefficients:
## (Intercept) PercRentBurdenE
## 0.572868 0.002289What does the income and ratio look like there?
Although these three are the most rent burdened, it isn’t obvious based on its rent to tax ratio and median household income, except for in Nelson county (which only has 5 observations in the tract)
- Richmond: Median Income is $53,216.5 and the Rent to Tax Ratio is 0.484
- Charlottesville: Median Income is $62,477.5 and the Rent to Tax Ratio is 0.4325
- Nelson: Median Income is $53,579 and the Rent to Tax Ratio is 0.71
How many are below the poverty level in this County? and which demographics?
We can reach the same conclusions here, with Richmond having the third highest percentage of those below the poverty level.
- Richmond: On average, 17.6% of the county is below the poverty level
and people who are Black and Asian have the highest average percent of
those below the poverty level.
- Charlottesville: On average, 15.3% of the county is below the
poverty level and people who are Hispanic or Latino and Asian have the
highest average percent of those below the poverty level.
- Nelson: On average, 12.9% of the county is below the poverty level and people who are Asian have the highest average percent of those below the poverty level (50%).
What is the income distribution in this census tract look like?
Which county has the most students (includes Undergraduate and Graduate students)?
No one county has a disproportionate amount of students compared to the other, however, Richmond, Charlottesville, and Henrico have the highest student populations
ACS_Housing_Data %>%
group_by(County) %>%
summarize(Median_perc_students = median(Perc_StudentsE, na.rm = TRUE))## # A tibble: 11 × 2
## County Median_perc_students
## <chr> <dbl>
## 1 Albemarle 5.21
## 2 Charlottesville 5.98
## 3 Chesterfield 5.43
## 4 Fluvanna 4.96
## 5 Greene 3.16
## 6 Henrico 5.59
## 7 Hopewell 4.84
## 8 Louisa 3.7
## 9 Nelson 4.96
## 10 Petersburg City 4.8
## 11 Richmond City 6.59*More than 50% of pop. in tracts in Richmond and Charlottesville city consist of students, but doesn’t seem like the case for Henrico!
##Students
#pal1 <- colorNumeric("Greens", domain = HousingDataSpatial$County, HousingDataSpatial$County)
#creating map and adding layers
perc_student_counties <- c("Richmond City", "Charlottesville", "Henrico")
Moststudents <- HousingDataSpatial %>%
filter(County %in% perc_student_counties)
pal1 <- colorNumeric("YlOrRd", Moststudents$Perc_StudentsE, reverse = TRUE) #the reverse argument reverses the color palette
Moststudents %>%
leaflet() %>%
addTiles() %>%
addPolygons(color = "black",
fillColor = ~pal1(Perc_StudentsE),
weight = 2,
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 3,
fillOpacity = 0.9,
bringToFront = T),
popup = paste0("County: ", Moststudents$County, "<br>",
"Tract: ", Moststudents$NAMELSAD, "<br>",
"Percentage of Rent Burden: ", Moststudents$Perc_StudentsE)) %>% #to add hovering functionality check what she put for the highlight argument and to add popups, check what she put for the popup argument
addLegend(pal = pal1,
values = ~Perc_StudentsE,
opacity = 0.7,
title = "Highest Percentage of Students in Charlottesville and Richmond regions for 2020",
position = "bottomleft")HousingDataSpatial %>%
filter(Perc_StudentsE >= 50) %>%
select(3,22) %>%
arrange(desc(Perc_StudentsE))## Simple feature collection with 8 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -78.52874 ymin: 37.22587 xmax: -77.41441 ymax: 38.05642
## CRS: +proj=longlat +datum=WGS84
## # A tibble: 8 × 3
## NAME.x Perc_StudentsE geometry
## <chr> <dbl> <MULTIPOLYGON [°]>
## 1 Census Tract 403, Richmond city, Vir… 90.5 (((-77.45652 37.54333, -…
## 2 Census Tract 109.04, Albemarle Count… 86.5 (((-78.52864 38.02705, -…
## 3 Census Tract 6, Charlottesville city… 71.9 (((-78.52363 38.0224, -7…
## 4 Census Tract 402.01, Richmond city, … 68.8 (((-77.45534 37.55372, -…
## 5 Census Tract 109.01, Albemarle Count… 65.9 (((-78.52472 38.0483, -7…
## 6 Census Tract 2.02, Charlottesville c… 60.4 (((-78.50342 38.03681, -…
## 7 Census Tract 1006, Chesterfield Coun… 60.4 (((-77.43459 37.23137, -…
## 8 Census Tract 305.01, Richmond city, … 54.9 (((-77.44895 37.54312, -…#ask soumya/claibourne why census tracts that aren't shaded ar being shown (esp since I filtered the dataframe)
#Tabset
###heading 1{.tabset}
####every heading under is a tabset!
#Can make the first tabs a leaflet and the third one as a scatterplot *Can see that Henrico Coutny has quite a few tracts with more than 10% of students and one with more than 15%, but it doesn’t compare to the major cities in VA
HousingDataSpatial %>%
filter(County == "Henrico") %>%
ggplot(aes(x = NAME.y, y = Perc_StudentsE)) +
geom_col() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
What does the income, and ratio look like there?
- Richmond: Median Income is $53,216.5 and the Rent to Tax Ratio is 0.484
- Charlottesville: Median Income is $62,477.5 and the Rent to Tax Ratio is 0.433
- Henrico: Median Income is $69,827 and the Rent to Tax Ratio is 0.659
How does this compare to the average gini index and who’s below poverty in each county?
Poverty_By_Race %>%
group_by(County) %>%
summarize(Median_perc_below_poverty = median(Perc_Total_BelowPovertyE, na.rm = TRUE), Median_Gini_Index = median(Gini_IndexE, na.rm = TRUE))## # A tibble: 11 × 3
## County Median_perc_below_poverty Median_Gini_Index
## <chr> <dbl> <dbl>
## 1 Albemarle 5.71 0.428
## 2 Charlottesville 15.3 0.480
## 3 Chesterfield 5.39 0.356
## 4 Fluvanna 3.76 0.407
## 5 Greene 9.22 0.390
## 6 Henrico 7.54 0.394
## 7 Hopewell 27.8 0.439
## 8 Louisa 10.6 0.426
## 9 Nelson 12.9 0.452
## 10 Petersburg City 22.1 0.437
## 11 Richmond City 17.7 0.448Poverty_Stats <- Poverty_By_Race %>%
filter(Gini_IndexE >= 0.5 & Perc_Total_BelowPovertyE >= 0.5) %>%
select(3,4,36,50) %>%
arrange(desc(Gini_IndexE))
knitr::kable(Poverty_Stats)| NAME | County | Gini_IndexE | Perc_Total_BelowPovertyE |
|---|---|---|---|
| Census Tract 201.03, Fluvanna County, Virginia | Fluvanna | 0.7173 | 6.53 |
| Census Tract 403, Richmond city, Virginia | Richmond City | 0.7044 | 66.57 |
| Census Tract 305.01, Richmond city, Virginia | Richmond City | 0.6917 | 58.62 |
| Census Tract 6, Charlottesville city, Virginia | Charlottesville | 0.6686 | 63.91 |
| Census Tract 404, Richmond city, Virginia | Richmond City | 0.6400 | 39.70 |
| Census Tract 207, Richmond city, Virginia | Richmond City | 0.6393 | 21.24 |
| Census Tract 7, Charlottesville city, Virginia | Charlottesville | 0.6153 | 21.08 |
| Census Tract 2008.05, Henrico County, Virginia | Henrico | 0.6129 | 32.49 |
| Census Tract 1009.38, Chesterfield County, Virginia | Chesterfield | 0.6044 | 0.79 |
| Census Tract 9501.02, Nelson County, Virginia | Nelson | 0.5953 | 17.73 |
| Census Tract 2009.08, Henrico County, Virginia | Henrico | 0.5929 | 8.58 |
| Census Tract 405, Richmond city, Virginia | Richmond City | 0.5811 | 14.40 |
| Census Tract 104.01, Albemarle County, Virginia | Albemarle | 0.5762 | 5.29 |
| Census Tract 210, Richmond city, Virginia | Richmond City | 0.5745 | 30.86 |
| Census Tract 204, Richmond city, Virginia | Richmond City | 0.5731 | 50.49 |
| Census Tract 8107, Petersburg city, Virginia | Petersburg City | 0.5605 | 22.63 |
| Census Tract 413, Richmond city, Virginia | Richmond City | 0.5551 | 17.66 |
| Census Tract 2.02, Charlottesville city, Virginia | Charlottesville | 0.5448 | 57.51 |
| Census Tract 109.01, Albemarle County, Virginia | Albemarle | 0.5433 | 29.63 |
| Census Tract 412, Richmond city, Virginia | Richmond City | 0.5417 | 32.50 |
| Census Tract 109.04, Albemarle County, Virginia | Albemarle | 0.5407 | 29.15 |
| Census Tract 4.01, Charlottesville city, Virginia | Charlottesville | 0.5317 | 18.69 |
| Census Tract 209, Richmond city, Virginia | Richmond City | 0.5309 | 8.30 |
| Census Tract 710.04, Richmond city, Virginia | Richmond City | 0.5306 | 50.90 |
| Census Tract 2010.03, Henrico County, Virginia | Henrico | 0.5291 | 18.17 |
| Census Tract 505, Richmond city, Virginia | Richmond City | 0.5284 | 2.57 |
| Census Tract 605.02, Richmond city, Virginia | Richmond City | 0.5225 | 8.58 |
| Census Tract 10, Charlottesville city, Virginia | Charlottesville | 0.5205 | 7.69 |
| Census Tract 101, Albemarle County, Virginia | Albemarle | 0.5178 | 6.29 |
| Census Tract 1009.26, Chesterfield County, Virginia | Chesterfield | 0.5125 | 1.94 |
| Census Tract 501, Richmond city, Virginia | Richmond City | 0.5099 | 8.22 |
| Census Tract 112.01, Albemarle County, Virginia | Albemarle | 0.5060 | 6.02 |
| Census Tract 8106, Petersburg city, Virginia | Petersburg City | 0.5018 | 36.43 |
| Census Tract 102.01, Richmond city, Virginia | Richmond City | 0.5017 | 7.02 |
| Census Tract 8113, Petersburg city, Virginia | Petersburg City | 0.5014 | 34.61 |
| Census Tract 109, Richmond city, Virginia | Richmond City | 0.5013 | 21.20 |
Poverty_By_Race %>%
ggplot(aes(x = Perc_Total_BelowPovertyE, y = Gini_IndexE)) +
geom_point(alpha = 0.3) +
geom_smooth(method = "lm") +
facet_wrap(~Region)
#model
lm(Gini_IndexE ~ Perc_Total_BelowPovertyE, data = Poverty_By_Race)##
## Call:
## lm(formula = Gini_IndexE ~ Perc_Total_BelowPovertyE, data = Poverty_By_Race)
##
## Coefficients:
## (Intercept) Perc_Total_BelowPovertyE
## 0.38135 0.00279lm(Perc_Total_BelowPovertyE ~ Gini_IndexE, data = Poverty_By_Race)##
## Call:
## lm(formula = Perc_Total_BelowPovertyE ~ Gini_IndexE, data = Poverty_By_Race)
##
## Coefficients:
## (Intercept) Gini_IndexE
## -17.26 72.31Which demographics are most likely to be below poverty and above (or at) in each county?
PovertyBy_Race_County <- Poverty_By_Race %>%
group_by(County) %>%
summarize(Median_Perc_WBelowPoverty = median((Perc_BelowPoverty_WhiteE), na.rm = TRUE), Median_Perc_BBelowPoverty = median((Perc_BelowPoverty_BlackE), na.rm = TRUE), Median_Perc_NativeAm_BelowPoverty = median((Perc_BelowPoverty_NativeAmE), na.rm = TRUE), Median_Perc_Asian_BelowPoverty = median((Perc_BelowPoverty_AsianE), na.rm = TRUE), Median_Perc_PacificIslander_BelowPoverty = median((Perc_BelowPoverty_PacificIslanderE), na.rm = TRUE), Median_Perc_HispanicLatino_BelowPoverty = median((Perc_BelowPoverty_HispanicLatinoE), na.rm = TRUE))
knitr::kable(PovertyBy_Race_County)| County | Median_Perc_WBelowPoverty | Median_Perc_BBelowPoverty | Median_Perc_NativeAm_BelowPoverty | Median_Perc_Asian_BelowPoverty | Median_Perc_PacificIslander_BelowPoverty | Median_Perc_HispanicLatino_BelowPoverty |
|---|---|---|---|---|---|---|
| Albemarle | 5.670 | 0.840 | 0.00 | 0.00 | 0 | 0.460 |
| Charlottesville | 11.020 | 14.575 | 0.00 | 19.48 | 0 | 17.900 |
| Chesterfield | 4.140 | 5.320 | 0.00 | 0.00 | 0 | 4.420 |
| Fluvanna | 3.730 | 3.690 | 0.00 | 0.00 | 0 | 0.000 |
| Greene | 6.595 | 9.340 | 0.00 | 2.38 | NA | 8.390 |
| Henrico | 5.860 | 7.080 | 0.00 | 0.00 | 0 | 0.865 |
| Hopewell | 20.820 | 33.050 | 0.00 | 0.00 | NA | 30.635 |
| Louisa | 9.000 | 20.730 | 0.00 | 0.00 | NA | 0.000 |
| Nelson | 4.500 | 2.750 | NA | 50.00 | NA | 0.000 |
| Petersburg City | 12.140 | 23.000 | 47.22 | 8.74 | 0 | 1.780 |
| Richmond City | 9.200 | 24.690 | 0.00 | 26.92 | 0 | 12.545 |
#graph! #Relationship btw those who are rent burdended and gini index?
#Scatterplot Matrix
#
#
# pbr_matrix <- ggpairs(Poverty_By_Race, mapping = aes(color = Region), columns = c(38, 39, 40, 43, 50, 36))
#
# pbr_matrix#
HDSpatial_Updated <- HousingDataSpatial %>%
mutate(county_tract = paste(COUNTYFP,TRACTCE, sep = ""))
#
cvl_rva_measures_Spatial <- full_join(cvl_rva_measures, HDSpatial_Updated, by = "county_tract") %>%
sf::st_as_sf() |>
sf::st_transform(crs = 4326)
#
pal2 <- colorNumeric("viridis", cvl_rva_measures_Spatial$iso_b_block, reverse = TRUE)
cvl_rva_measures_Spatial %>%
leaflet() %>%
addTiles() %>%
addPolygons(color = "black",
fillColor = ~pal2(iso_b_block),
fillOpacity = 0.6,
weight = 2,
highlight = highlightOptions(
weight = 3,
fillOpacity = 0.9,
bringToFront = T),
popup = paste0("County: ", cvl_rva_measures_Spatial$County, "<br>",
"Tract: ", cvl_rva_measures_Spatial$NAME.x, "<br>",
"Isolation Index: ", cvl_rva_measures_Spatial$iso_b_block)) %>%
addLegend(pal = pal2,
values = ~iso_b_block,
opacity = 0.7,
title = "Black Isolation Index (2020)",
position = "bottomleft")# Code above not working, all appearing as the same color...
#FOR THE POSTER
Iso_index_cvl_rva <- cvl_rva_measures_Spatial %>%
ggplot() +
geom_sf(aes(fill=iso_b_block)) +
scale_fill_viridis_c() +
scale_color_viridis_c() +
ggtitle("Black Isolation Index in Charlottesville and Richmond, VA", subtitle = "Data from the 2020 American Community Survey (ACS)") +
labs(fill = "Isolation Index")
Iso_index_rva <- cvl_rva_measures_Spatial %>%
filter(Region == "Richmond") %>%
ggplot() +
geom_sf(aes(fill=iso_b_block)) +
scale_fill_viridis_c() +
scale_color_viridis_c() +
ggtitle("Black Isolation Index in Richomond", subtitle = "Data from the 2020 American Community Survey (ACS)") +
labs(fill = "Isolation Index")
ggsave("Black Isolation Index in Richmond.png", Iso_index_cvl_rva)#Relationshiop btw Rent exploitation and segregation meausres
lm(RentTaxRatio ~ dissim_wb_block, data = cvl_rva_measures_Spatial) #-0.4407##
## Call:
## lm(formula = RentTaxRatio ~ dissim_wb_block, data = cvl_rva_measures_Spatial)
##
## Coefficients:
## (Intercept) dissim_wb_block
## 0.8253 -0.4408lm(RentTaxRatio ~ iso_b_block, data = cvl_rva_measures_Spatial) #0.3226##
## Call:
## lm(formula = RentTaxRatio ~ iso_b_block, data = cvl_rva_measures_Spatial)
##
## Coefficients:
## (Intercept) iso_b_block
## 0.5185 0.3226cvl_rva_measures_Spatial %>%
ggplot(aes(x = dissim_wb_block, y = RentTaxRatio)) +
geom_point(alpha = 0.3) +
geom_smooth(method = "lm") +
facet_wrap(~Region)
cvl_rva_measures_Spatial %>%
ggplot(aes(x = iso_b_block, y = RentTaxRatio)) +
geom_point(alpha = 0.3) +
geom_smooth(method = "lm") +
facet_wrap(~Region)
Plots from Previous RMarkdown on distribution
ACS_Housing_Data %>%
ggplot(aes(ACS_Housing_Data$PercRentBurdenE, fill = Region)) +
geom_histogram()
#distrubtion of the rent tax ratio as a whole
ACS_Housing_Data %>%
ggplot(aes(ACS_Housing_Data$RentTaxRatio)) +
geom_histogram()
#distrubtion of the rent tax ratio by county
ACS_Housing_Data %>%
ggplot(aes(ACS_Housing_Data$RentTaxRatio)) +
geom_histogram() +
facet_wrap(~County)
#distrubtion of the rent tax ratio by county (boxplot)
ACS_Housing_Data |>
ggplot() +
aes(x = County, y = RentTaxRatio) +
geom_boxplot() 